Over the last several years, the field of natural language processing has
been propelled forward by an explosion in the use of deep learning models. This
survey provides a brief introduction to the field and a quick overview of deep
learning architectures and methods. It then sifts through the plethora of
recent studies and summarizes a large assortment of relevant contributions.
Analyzed research areas include several core linguistic processing issues in
addition to a number of applications of computational linguistics. A discussion
of the current state of the art is then provided along with recommendations for
future research in the field.

本文简要介绍了自然语言处理领域和深度学习架构和方法，并从最近的研究中总结了大量相关贡献，对核心语言处理问题和计算语言学的多个应用进行了分析，并提供了当前技术水平的讨论和未来研究的建议。

深度学习在自然语言处理中的应用调查

A Survey of the Usages of Deep Learning in Natural Language Processing

Recurrent neural networks (RNNs) have achieved impressive results in a
variety of linguistic processing tasks, suggesting that they can induce
non-trivial properties of language. We investigate here to what extent RNNs
learn to track abstract hierarchical syntactic structure. We test whether RNNs
trained with a generic language modeling objective in four languages (Italian,
English, Hebrew, Russian) can predict long-distance number agreement in various
constructions. We include in our evaluation nonsensical sentences where RNNs
cannot rely on semantic or lexical cues ("The colorless green ideas I ate with
the chair sleep furiously"), and, for Italian, we compare model performance to
human intuitions. Our language-model-trained RNNs make reliable predictions
about long-distance agreement, and do not lag much behind human performance. We
thus bring support to the hypothesis that RNNs are not just shallow-pattern
extractors, but they also acquire deeper grammatical competence.

本文研究了递归神经网络中选择性跟踪语言抽象层级结构的能力，并测试了递归神经网络在四种语言（意大利语、英语、希伯来语、俄语）中使用语言模型目标进行训练是否能够预测各种结构中的长距离数字一致性，结果表明递归神经网络能够可靠地预测长距离一致性而不依赖于词汇或语义信号，支持递归神经网络不仅是浅层模式学习器，而且还能获得较深语法能力的假设。